SQLore
FeaturesPricingDownloadAboutBlogContact
Start freeStart for free
SQLore
HomeFeaturesSolutionsPricingDownloadCompareFAQAboutBlogContact

SQLore © 2026

TermsPrivacyRefunds
Download for macOSDownload for WindowsSign in
  1. Home
  2. Solutions
  3. The interface
The interface

AI Database Chat, Where The Second Question Is The Real One

Nobody gets the question right first time. A chat holds what has already been established, so refining an answer costs a sentence rather than a rewrite.

Download SQLoreSee all features

What is AI database chat?

AI database chat is a conversational interface to a database, where each question is answered with real rows — described in plain English, drawn as a chart or a table, and shown with the SQL behind them — and follow-up questions inherit the context of the ones before. SQLore keeps that context across a conversation, saves it, and lets you re-run any earlier answer against current data.

Why a chat beats a query box for exploration

Exploratory analysis is a sequence of small corrections. You ask something roughly right, look at the shape of the answer — literally, because the answer is drawn as a chart — and adjust. In a query box every adjustment means editing SQL you just read; in a chat it means a clause. "Break that down by plan." "Drop the internal accounts." "Same thing for last year." The tables, joins and filters already agreed on stay agreed on.

Context is a liability if it is invisible

The risk in a stateful interface is that you lose track of what is still applied — a filter set four questions ago quietly shaping an answer you are about to take to a meeting. That is why every answer arrives with its full SQL rather than a diff. The query is the state, written out, and reading it takes a second.

Conversations are kept, searchable and re-runnable

The query you worked out in March is still there in September, and running it again gives you September's numbers rather than March's cached ones. In practice a saved conversation becomes a lightweight report: the reasoning, the query and the current answer in one place, without anyone having to promote it into a dashboard.

What it does when it does not know

Two plausible date columns, or two tables that both look like the orders table, is ambiguity the schema cannot settle. It comes back as a question rather than a confident guess — and once you have answered it, you can write it down as a rule so the same question is not asked next month.

Frequently asked questions

Does it remember context between questions?

Within a conversation, yes — the tables, joins and filters established by earlier questions carry forward, which is what makes a follow-up a sentence rather than a rewrite. Each answer still shows its complete SQL, so the accumulated context is always visible rather than implied.

Can I go back to a conversation from months ago?

Conversations are saved, searchable and re-runnable. Re-running an old question executes it against your database now, so you get current data rather than a stored snapshot.

Can I share a conversation with a colleague?

The query is the shareable artefact, and it is shown with every answer — copy it and it runs anywhere. For teams that want a shared instance rather than passed-around SQL, the self-hosted server build is the route; ask us about it.

More answers on the main FAQ, or ask us directly.

Related

PostgreSQL

Chat With Your PostgreSQL Database

Read more

MySQL

Chat With Your MySQL Database

Read more

For analysis

An AI Database Query Tool For The Questions Between Dashboards

Read more

Every page in this section covers a different question about the same product. See all solutions.

SQLore platform

Stop Writing The
Same Three Joins

Start free, upgrade when you are asking enough questions for it to matter.

Start for freeContact sales
sqlore.com

New chat

Search chats

TODAY

Orders by month

PREVIOUS 7 DAYS

Top products by revenue

PREVIOUS 30 DAYS

Repeat customer rate

Revenue by category

Average order value

Refunds by reason

Customers by city

Cart abandonment

Slowest shipping lanes

Discount impact on margin

Inventory turnover

Order statistics

SQLoredesktopretail_db · default⌘K

Order statistics for this year

Reasoning — 2 steps, 2 queries

Total orders · Delivered · In transit · Cancelled

1 row · 10 ms

Stat

15,600

Total orders

13,942

Delivered

1,412

In transit

246

Cancelled

orders by category

4 rows · 11 ms

LineDonutBar
8,4006,3004,2002,1000
ACCESSORIESPERIPHERALSFURNITUREDISPLAYS

There are 15,600 orders in retail_db for the year to date.

  • Status: 13,942 have been delivered and 1,412 are still in transit.
  • Category: most sit in Accessories (8,420), followed by Peripherals (4,180), Furniture (2,140) and Displays (860).

Revenue by payment method

Reasoning — 1 step, 1 query

Ask about your database…

Answers come from SQL run against your database.